Objectives: To investigate the predictive ability of radiomics signature to predict the prognosis of early-stage primary lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I).
Materials And Methods: This study included consecutive patients with lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I) and divided them into two groups: good prognosis group and poor prognosis group. The association between the radiomics signature and prognosis was explored. An integrative radiomics model was constructed to demonstrate the value of the radiomics signature for individualized prognostic prediction.
Results: Six radiomics features were significantly different between the two prognosis groups and were used to construct a radiomics model. On the training and test sets, the area under the receiver operating characteristic curve value of the radiomics model in discriminating between the two groups were 0.946 and 0.888, respectively, and those of the pathological model were 0.761 and 0.798, respectively. A radiomics nomogram combining sex, tumor size and rad-score was built.
Conclusion: The radiomics signature has potential utility in estimating the prognosis of patients with pathological stage I lung adenocarcinoma (≤3 cm), potentially enabling a step forward in precision medicine.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406362 | PMC |
| http://dx.doi.org/10.3390/diagnostics12081907 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Habitat-based MRI radiomics to predict the origin of brain metastasis.
Med Phys
January 2025
Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, P. R. China.
Background: This study aims to explore the value of habitat-based magnetic resonance imaging (MRI) radiomics for predicting the origin of brain metastasis (BM).
Purpose: To investigate whether habitat-based radiomics can identify the metastatic tumor type of BM and whether an imaging-based model that integrates the volume of peritumoral edema (VPE) can enhance predictive performance.
Methods: A primary cohort was developed with 384 patients from two centers, which comprises 734 BM lesions.
Preoperative Computed Tomography Radiomics-Based Models for Predicting Microvascular Invasion of Intrahepatic Mass-Forming Cholangiocarcinoma.
J Comput Assist Tomogr
November 2024
From the Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu Province, China.
Objectives: The aim of the study is to investigate the ability of preoperative CT (Computed Tomography)-based radiomics signature to predict microvascular invasion (MVI) of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models.
Materials And Methods: Preoperative clinical data, basic CT features, and radiomics features of 121 IMCC patients (44 with MVI and 77 without MVI) were retrospectively reviewed. The loading and display of CT images, delineation of the volume of interest, and feature extraction were performed using 3D Slicer.
An MRI-based radiomics nomogram for preoperative prediction of Ki-67 index in nasopharyngeal carcinoma: a two-center study.
Front Oncol
December 2024
Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, China.
Background: The expression level of Ki-67 in nasopharyngeal carcinoma (NPC) affects the prognosis and treatment options of patients. Our study developed and validated an MRI-based radiomics nomogram for preoperative evaluation of Ki-67 expression levels in nasopharyngeal carcinoma (NPC).
Methods: In all, 133 patients with pathologically-confirmed (post-operatively) NPC who underwent MRI examination in one of two medical centers.
Neural Network and Logistic Regression Models Based on Ultrasound Radiomics and Clinical-Pathological Features to Predict Occult Level II Lymph Node Metastasis in Papillary Thyroid Carcinoma.
Acad Radiol
January 2025
Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China (J-W.F., H.L.); Zhejiang Engineering Research Center of Cognitive Healthcare, Sir Run Run Shaw Hospital,School of Medicine, Zhejiang University, Hangzhou, China (H.L.); College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China (H.L.). Electronic address:
Rationale And Objectives: Papillary thyroid carcinoma (PTC) often metastasizes to lateral cervical lymph nodes, especially in level II. This study aims to develop predictive models to identify level II lymph node metastasis (LNM), guiding selective neck dissection (SND) to minimize unnecessary surgery and morbidity in low-risk patients.
Methods: A retrospective cohort of 313 PTC patients who underwent modified radical neck dissection (MRND) between October 2020 and January 2023 was analyzed.
Explainable PET-Based Habitat and Peritumoral Machine Learning Model for Predicting Progression-free Survival in Clinical Stage IA Pure-Solid Non-small Cell Lung Cancer: A Two-center Study.
Acad Radiol
January 2025
Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Key Laboratory of Novel Nuclide Technologies on Precision Diagnosis and Treatment & Clinical Transformation of Wenzhou City, China (K.T.). Electronic address:
Rationale And Objectives: This study aimed to develop and validate machine learning (ML) models utilizing positron emission tomography (PET)-habitat of the tumor and its peritumoral microenvironment to predict progression-free survival (PFS) in patients with clinical stage IA pure-solid non-small cell lung cancer (NSCLC).
Materials And Methods: 234 Patients who underwent lung resection for NSCLC from two hospitals were reviewed. Radiomic features were extracted from both intratumoral, peritumoral and habitat regions on PET.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!